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Dataset Title:  Growth rates across temperatures for 11 new isolates of marine Synechococcus
from Narragansett Bay, July 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_782314)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
Variable ?   Optional
Constraint #1 ?
Constraint #2 ?
   Minimum ?
   Maximum ?
 Isolate (unitless) ?          "LA101"    "LA31"
 Temp (Temperature, degrees Celsius) ?          9    35
 growth_rate_mean (cells/day) ?          -0.32915179    0.724674784
 growth_rate_StDev (cells/day) ?          2.7448E-4    0.438579435
Server-side Functions ?
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Isolate {
    String bcodmo_name "taxon_code";
    String description "sample identifier; renamed from Sample_Code";
    String long_name "Isolate";
    String units "unitless";
  Temp {
    Byte _FillValue 127;
    Byte actual_range 9, 35;
    String bcodmo_name "temperature";
    String description "temperature during growth rates experiments";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  growth_rate_mean {
    Float64 _FillValue NaN;
    Float64 actual_range -0.32915179, 0.724674784;
    String bcodmo_name "growth";
    String description "mean growth rate";
    String long_name "Growth Rate Mean";
    String units "cells/day";
  growth_rate_StDev {
    Float64 _FillValue NaN;
    Float64 actual_range 2.7448e-4, 0.438579435;
    String bcodmo_name "growth";
    String description "standard deviation of growth rates";
    String long_name "Growth Rate St Dev";
    String units "cells/day";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Natural seawater was enriched for photoautotrophs and split into multiple
temperatures for two weeks. After the enrichment period, Synechococcus was
isolated from each temperature. Each isolate's thermal niche was measured
through a series of lab experiments and sequenced.
Methods:\\u00a0Thermal niches were calculated by measuring each strain\\u2019s
thermal performance curve. This was done by acclimating aliquots of each
culture for two weeks to a broad range of temperatures between 9\\u00b0 and
33\\u00b0. Temperatures >33\\u00b0 were added as needed for strains able to grow
at these levels.\\u00a0 This temperature range was chosen because it exceeds
the current summer high and low temperatures in Narragansett Bay, and
encompasses the projected warmer temperatures expected in coming decades.
Strains were grown at each temperature in triplicate 8 ml borosilicate vials
containing 5ml of F/2 medium. Biomass was recorded every two days using in
vivo chlorophyll a fluorescence measured on a Turner AU-10 fluorometer (Turner
Designs Inc., Sunnyvale, CA, USA), and growth rates and Eppley-Norberg thermal
performance curves (Norberg 2004) were calculated in R (Team 2019) using the
package growthTools (DOI:10.5281/zenodo.3634918). Rare cultures containing
contaminants (verified using fluorescence microscopy) were excluded from the
dataset. In two strains for low temperature treatments, LA20 and LA27, after
two weeks of acclimation no Synechococcus cells were observed in the culture,
so the growth rate was set to zero for these cultures. The results of these
thermal performance curves are hereafter referred to as a strain\\u2019s
    String awards_0_award_nid "712792";
    String awards_0_award_number "OCE-1638804";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638804";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Synechococcus growth rates 
   Growth rates across temperatures for 11 new isolates of marine Synechococcus 
   PI: D. Hutchins (USC) 
   version date: 2019-11-20";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String dataset_current_state "Final and no updates";
    String date_created "2019-11-21T13:53:09Z";
    String date_modified "2020-04-27T20:32:04Z";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.782314.1";
    String history 
"2024-07-16T15:48:36Z (local files)
2024-07-16T15:48:36Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_782314.html";
    String infoUrl "https://www.bco-dmo.org/dataset/782314";
    String institution "BCO-DMO";
    String instruments_0_acronym "Turner Fluorometer 10-AU";
    String instruments_0_dataset_instrument_description "Used to measure fluorescence for growth rate calculations.";
    String instruments_0_dataset_instrument_nid "783234";
    String instruments_0_description "The Turner Designs 10-AU Field Fluorometer is used to measure Chlorophyll fluorescence. The 10AU Fluorometer can be set up for continuous-flow monitoring or discrete sample analyses. A variety of compounds can be measured using application-specific optical filters available from the manufacturer. (read more from Turner Designs, turnerdesigns.com, Sunnyvale, CA, USA)";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0393/";
    String instruments_0_instrument_name "Turner Designs Fluorometer 10-AU";
    String instruments_0_instrument_nid "464";
    String instruments_0_supplied_name "Turner 10-AU Fluorometer (Turner Designs, CA)";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dev, dmo, erddap, growth, growth_rate_mean, growth_rate_StDev, isolate, management, mean, oceanography, office, preliminary, rate, Temp, temperature";
    String license "https://www.bco-dmo.org/dataset/782314/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/782314";
    String param_mapping "{'782314': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/782314/parameters";
    String people_0_affiliation "University of Southern California";
    String people_0_affiliation_acronym "USC";
    String people_0_person_name "David A. Hutchins";
    String people_0_person_nid "51048";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Nancy Copley";
    String people_1_person_nid "50396";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Phytoplankton Community Responses";
    String projects_0_acronym "Phytoplankton Community Responses";
    String projects_0_description 
"NSF Award Abstract:
Photosynthetic marine microbes, phytoplankton, contribute half of global primary production, form the base of most aquatic food webs and are major players in global biogeochemical cycles. Understanding their community composition is important because it affects higher trophic levels, the cycling of energy and elements and is sensitive to global environmental change. This project will investigate how phytoplankton communities respond to two major global change stressors in aquatic systems: warming and changes in nutrient availability. The researchers will work in two marine systems with a long history of environmental monitoring, the temperate Narragansett Bay estuary in Rhode Island and a subtropical North Atlantic site near Bermuda. They will use field sampling and laboratory experiments with multiple species and varieties of phytoplankton to assess the diversity in their responses to different temperatures under high and low nutrient concentrations. If the diversity of responses is high within species, then that species may have a better chance to adapt to rising temperatures and persist in the future. Some species may already be able to grow at high temperatures; consequently, they may become more abundant as the ocean warms. The researchers will incorporate this response information in mathematical models to predict how phytoplankton assemblages would reorganize under future climate scenarios. Graduate students and postdoctoral associates will be trained in diverse scientific approaches and techniques such as shipboard sampling, laboratory experiments, genomic analyses and mathematical modeling. The results of the project will be incorporated into K-12 teaching, including an advanced placement environmental science class for underrepresented minorities in Los Angeles, data exercises for rural schools in Michigan and disseminated to the public through an environmental journalism institute based in Rhode Island.
Predicting how ecological communities will respond to a changing environment requires knowledge of genetic, phylogenetic and functional diversity within and across species. This project will investigate how the interaction of phylogenetic, genetic and functional diversity in thermal traits within and across a broad range of species determines the responses of marine phytoplankton communities to rising temperature and changing nutrient regimes. High genetic and functional diversity within a species may allow evolutionary adaptation of that species to warming. If the phylogenetic and functional diversity is higher across species, species sorting and ecological community reorganization is likely. Different marine sites may have a different balance of genetic and functional diversity within and across species and, thus, different contribution of evolutionary and ecological responses to changing climate. The research will be conducted at two long-term time series sites in the Atlantic Ocean, the Narragansett Bay Long-Term Plankton Time Series and the Bermuda Atlantic Time Series (BATS) station. The goal is to assess intra- and inter-specific genetic and functional diversity in thermal responses at contrasting nutrient concentrations for a representative range of species in communities at the two sites in different seasons, and use this information to parameterize eco-evolutionary models embedded into biogeochemical ocean models to predict responses of phytoplankton communities to projected rising temperatures under realistic nutrient conditions. Model predictions will be informed by and tested with field data, including the long-term data series available for both sites and in community temperature manipulation experiments. This project will provide novel information on existing intraspecific genetic and functional thermal diversity for many ecologically and biogeochemically important phytoplankton species, estimate generation of new genetic and functional diversity in evolution experiments, and develop and parameterize novel eco-evolutionary models interfaced with ocean biogeochemical models to predict future phytoplankton community structure. The project will also characterize the interaction of two major global change stressors, warming and changing nutrient concentrations, as they affect phytoplankton diversity at functional, genetic, and phylogenetic levels. In addition, the project will develop novel modeling methodology that will be broadly applicable to understanding how other types of complex ecological communities may adapt to a rapidly warming world.";
    String projects_0_end_date "2020-09";
    String projects_0_geolocation "Narragansett Bay, RI and Bermuda, Bermuda Atlantic Time-series Study (BATS)";
    String projects_0_name "Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients";
    String projects_0_project_nid "712787";
    String projects_0_start_date "2016-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Growth rates across temperatures for 11 new isolates of marine Synechococcus from Narragansett Bay, July 2017.";
    String title "Growth rates across temperatures for 11 new isolates of marine Synechococcus from Narragansett Bay, July 2017";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.5";


Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
For example,
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

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